This invention relates generally to services such as messaging and scheduling, and more particularly to learning for services such as messaging and scheduling.
Computer applications such as messaging and scheduling applications have become important applications in many computer users"" lives. Messaging programs generally allow a user to send and receive electronic mail (e.g., messages) to and from other computer users, for example, over a local- or a wide-area network, or over an intranet, extranet, or the Internet. Scheduling programs generally allow a user to track appointments in a calendar. More sophisticated scheduling programs allow one user to schedule a group meeting with other computer usersxe2x80x94checking the latter users"" schedule availability, and receiving confirmation from the users upon them accepting or rejecting the group meeting appointment.
Within the prior art, however, messaging and scheduling programs are generally not very well integrated, even if they are components within the same computer program. For example, a user may receive a message from a colleague stating xe2x80x9cLooking forward to seeing you at 2 p.m. on Thursday.xe2x80x9d Generally, however, the prior art does not provide for automatically directing the scheduling program to make a meeting appointment at 2 p.m. on Thursday. Instead, typically the user who has received the message has to open the scheduling program, access Thursday""s calendar, and manually enter an appointment at 2 p.m. on Thursday""s calendar. Because of the many steps required to go from reading the message within the messaging program to entering the information into the scheduling program, many users choose not to even use scheduling programs, or to only use them sparingly.
For these and other reasons, there is a need for the present invention.
The invention relates to learning for automated services, such as messaging and scheduling. In one embodiment, a computer-implemented method first determines a text to analyze. The method then determines an action probability based on the text, and based on the action probability, selects one of the following options: (1) inaction, (2) automatic action, or (3) suggested action with user approval. Upon the method selecting either the (1) automatic action option or the (2) suggested action with user approval optionxe2x80x94the latter also in conjunction with receiving actual user approvalxe2x80x94the method performs an action based on the text. In one embodiment, the method waits a predetermined time prior to selecting the option (in one embodiment), which can be determined by performing a statistical regression as to the predetermined time that should be waited based on a length of each text. Furthermore, in one embodiment the action probability is determined by a text classification system, such that the system is continually trained based on each text inputted thereinto.
Embodiments of the invention provide for advantages not found within the prior art. For example, in the context of scheduling appointments based on the text input, the method can perform an action based on the text, upon determining the action probability of the text. Based on the action probability the method determines if it should do nothing (i.e., corresponding to a low probability), do something automatically (i.e., corresponding to a high probability), or suggest an action, but do not do it automatically (i.e., corresponding to a medium probability). Thus, one embodiment of the invention effectively links scheduling with messaging automatically. It is noted that the invention itself is not limited to the application of scheduling and messaging, however; for example, other actions that can be based on the text analyzed including forwarding, paging, routing and moving, as those of ordinary skill within the art can appreciate.
Furthermore, embodiments of the invention provide for learning for the services that are automated. For example, in one embodiment, an action option is not selected until a predetermined time is waited based on a statistical regression of the length of all the texts inputted. Thus, this embodiment learns how long it generally takes for the user to read a message of a given length, and does not interrupt the user with an automated service (viz., an action) or a dialog regarding a proposed automated service until it believes that the user has finished reading the message. As another example, in one embodiment, a text classification system that can be used to determine the action probability is continually trained by each text inputted thereinto. Thus, a pretrained system becomes adjusted to the user""s own preferences as the user uses the system.